How to Separate Absolute and Relative Error Components: Interval Case

Authors

Christian Servin, El Paso Community CollegeFollow
Olga Kosheleva, University of Texas at El PasoFollow
Vladik Kreinovich, University of Texas at El PasoFollow

Publication Date

10-2020

Comments

Technical Report: UTEP-CS-20-101a

Published in: Franco Pavese, Alistair B. Forbes, Nien Fan Zhang, and Anna G. Chunovkina (eds.), Advanced Mathematical and Computational Tools in Metrology and Testing XII, World Scientific, Singapore, 2021, pp. 390-405.

Abstract

Usually, measurement errors contain both absolute and relative components. To correctly gauge the amount of measurement error for all possible values of the measured quantity, it is important to separate these two error components. For probabilistic uncertainty, this separation can be obtained by using traditional probabilistic techniques. The problem is that in many practical situations, we do not know the probability distribution, we only know the upper bound on the measurement error. In such situations of interval uncertainty, separation of absolute and relative error components is not easy. In this paper, we propose a technique for such a separation based on the maximum entropy approach, and we provide feasible algorithms -- both sequential and parallel -- for the resulting separation.